Search Results for author:
Found 11 papers, 1 papers with code
Interpretable Deep Multimodal Image Super-Resolution
Multimodal image super-resolution (SR) is the reconstruction of a high resolution image given a low-resolution observation with the aid of another image modality.
Multimodal Deep Unfolding for Guided Image Super-Resolution
The deep unfolding architecture is used as a core component of a multimodal framework for guided image super-resolution.
Multimodal Image Super-resolution via Deep Unfolding with Side Information
Deep learning methods have been successfully applied to various computer vision tasks.
Deep Coupled-Representation Learning for Sparse Linear Inverse Problems with Side Information
In linear inverse problems, the goal is to recover a target signal from undersampled, incomplete or noisy linear measurements.
Matrix Factorization via Deep Learning
Matrix completion is one of the key problems in signal processing and machine learning.
Matrix Completion With Variational Graph Autoencoders: Application in Hyperlocal Air Quality Inference
Existing inference methods typically use low spatial resolution data collected by fixed monitoring stations and infer the concentration of air pollutants using additional types of data, e. g., meteorological and traffic information.
Regularizing Autoencoder-Based Matrix Completion Models via Manifold Learning
Specifically, we propose an autoencoder-based matrix completion model that performs prediction of the unknown matrix values as a main task, and manifold learning as an auxiliary task.
Extendable Neural Matrix Completion
Matrix completion is one of the key problems in signal processing and machine learning, with applications ranging from image pro- cessing and data gathering to classification and recommender sys- tems.
Twitter User Geolocation using Deep Multiview Learning
Predicting the geographical location of users on social networks like Twitter is an active research topic with plenty of methods proposed so far.
Multiview Deep Learning for Predicting Twitter Users' Location
In the context of Twitter user geolocation, we realize MENET with textual, network, and metadata features.
Deep Learning Sparse Ternary Projections for Compressed Sensing of Images
Compressed sensing (CS) is a sampling theory that allows reconstruction of sparse (or compressible) signals from an incomplete number of measurements, using of a sensing mechanism implemented by an appropriate projection matrix.
